Towards a new generation of reflection models for precision measurements of accreting black holes

TL;DR

This paper reviews the progress in X-ray reflection modeling for black holes and introduces efforts to develop advanced models using machine learning to meet the demands of upcoming high-quality observational data.

Contribution

It presents the development of a new generation of reflection models employing machine learning techniques for improved accuracy.

Findings

Current models are insufficient for next-generation data

Machine learning models can enhance reflection spectrum accuracy

Progress in theoretical and observational reflection studies

Abstract

Blurred reflection features are commonly observed in the X-ray spectra of accreting black holes. In the presence of high-quality data and with the correct astrophysical model, X-ray reflection spectroscopy is a powerful tool to probe the strong gravity region of black holes, study the morphology of the accreting matter, measure black hole spins, and test Einstein's theory of General Relativity in the strong field regime. In the past 10-15 years, there has been significant progress in the development of the analysis of these reflection features, thanks to both more sophisticated theoretical models and new observational data. However, the next generation of X-ray missions (e.g. eXTP, Athena, HEX-P) promises to provide unprecedented high-quality data, which will necessarily require more accurate synthetic reflection spectra than those available today. In this talk, I will review the state-of-the-art in reflection modeling and I will present current efforts to develop a new generation of reflection models with machine learning techniques.